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A socially inspired optimization algorithm for multi-extremal function optimization: team innovation algorithm
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AI
DOI：10.1007/s00500-025-10880-w.png)
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Abstract
En 中文
Some meta-heuristic algorithms are unable to solve the multi-extremal function optimization problem, so this paper proposes a team innovation algorithm (TIA) based on the process of team innovation. The algorithm simulates the hierarchy of team structures consisting of team leaders, team members, and backup members, as well as the process of innovation through team structure division, team members research, and research direction correction. The algorithm is suitable for multi-extremal function optimization problems. by constructing different group leaders to simulate multiple unknown optimal solutions that may exist in the optimization process. An adaptive search range adjustment method is established by simulating the research direction correction process, and the search range is automatically adjusted in the optimization process. 12 CEC2022 benchmark functions and 3 multiple-solution functions are used to benchmark TIA and compare with different meta-heuristic algorithms. The experimental results show that TIA can provide very competitive results in both single objective and multi-objective optimization problems with good optimization accuracy.
Keywords:
TIA
Group
Optimization
Social-behavior
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W